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class="post-meta-separator">|</span><span class="post-meta-pv-cv" id="" data-flag-title="Sololearn 自学机器学习（3） Pandas 数据读取与处理"><i class="far fa-eye fa-fw post-meta-icon"></i><span class="post-meta-label">阅读量:</span><span id="busuanzi_value_page_pv"><i class="fa-solid fa-spinner fa-spin"></i></span></span></div></div></div></header><main class="layout" id="content-inner"><div id="post"><article class="post-content" id="article-container"><h1 id="什么是-Pandas"><a href="#什么是-Pandas" class="headerlink" title="什么是 Pandas"></a>什么是 Pandas</h1><p>这整个系列教程将使用机器学习中最常用的编程语言 —— Python。Python 的社群特别强大，里面有无数个有用的且效率高的模组可以用来进行数据处理。包括今天的主角：Pandas</p>
<p>Pandas 是 Python 中用来读取和操作数据的一个库，它可以将数据转换成人们看得懂的表格模式来将数据进行输出，同时它也可以以数值方式对数据进行解释，从而使用户可以对数据进行各种计算。</p>
<div class="note info flat"><p>我们将 Pandas 的数据表称为 DataFrame</p>
</div>
<h1 id="读取数据"><a href="#读取数据" class="headerlink" title="读取数据"></a>读取数据</h1><p>现在开始使用 <code>pandas</code> 来读取数据，我们习惯将其命名为 <code>pd</code>，一遍后续能够快速调用</p>
<figure class="highlight python"><table><tr><td class="code"><pre><span class="line"><span class="keyword">import</span> pandas <span class="keyword">as</span> pd</span><br></pre></td></tr></table></figure>
<p>我们将对泰坦尼克号的数据集进行处理，在 <a href="/uploads/@files/titanic-dataset/train.csv" download>titanic.csv</a> 数据集中，记录了所有泰坦尼克号乘客的信息，包括以下几种（字段很多，就没一一列举了）：</p>
<ul>
<li>幸存情况</li>
<li>舱位等级</li>
<li>姓名、性别、年龄……等</li>
</ul>
<div class="table-container">
<table>
<thead>
<tr>
<th>PassengerId</th>
<th>Survived</th>
<th>Pclass</th>
<th>Lname</th>
<th>Name</th>
<th>Sex</th>
<th>Age</th>
<th>SibSp</th>
<th>Parch</th>
<th>Ticket</th>
<th>Fare</th>
<th>Cabin</th>
<th>Embarked</th>
</tr>
</thead>
<tbody>
<tr>
<td>1</td>
<td>0</td>
<td>3</td>
<td>Braund</td>
<td>Mr. Owen Harris</td>
<td>male</td>
<td>22</td>
<td>1</td>
<td>0</td>
<td>A/5 21171</td>
<td>7.25</td>
<td></td>
<td>S</td>
</tr>
<tr>
<td>2</td>
<td>1</td>
<td>1</td>
<td>Cumings</td>
<td>Mrs. John Bradley (Florence Briggs Thayer)</td>
<td>female</td>
<td>38</td>
<td>1</td>
<td>0</td>
<td>PC 17599</td>
<td>71.2833</td>
<td>C85</td>
<td>C</td>
</tr>
<tr>
<td>3</td>
<td>1</td>
<td>3</td>
<td>Heikkinen</td>
<td>Miss. Laina</td>
<td>female</td>
<td>26</td>
<td>0</td>
<td>0</td>
<td>STON/O2. 3101282</td>
<td>7.925</td>
<td></td>
<td>S</td>
</tr>
<tr>
<td>4</td>
<td>1</td>
<td>1</td>
<td>Futrelle</td>
<td>Mrs. Jacques Heath (Lily May Peel)</td>
<td>female</td>
<td>35</td>
<td>1</td>
<td>0</td>
<td>113803</td>
<td>53.1</td>
<td>C123</td>
<td>S</td>
</tr>
<tr>
<td>5</td>
<td>0</td>
<td>3</td>
<td>Allen</td>
<td>Mr. William Henry</td>
<td>male</td>
<td>35</td>
<td>0</td>
<td>0</td>
<td>373450</td>
<td>8.05</td>
<td></td>
<td>S</td>
</tr>
</tbody>
</table>
</div>
<p>我们将使用 pandas 对数据进行读取，可以通过 pandas 的 <code>read_csv</code> API 对数据进行读取，并转换成 pandas DataFrame 对象：</p>
<figure class="highlight python"><table><tr><td class="code"><pre><span class="line">df = pd.read_csv(<span class="string">&#x27;train.csv&#x27;</span>)</span><br></pre></td></tr></table></figure>
<div class="note info flat"><p>由于 Titanic Dataset 经常作为机器学习的学习用途，所以将其分为训练集和测试集两种，上方链接下载的就是训练集 <code>train.csv</code>，所以读取的是 train.csv 而非 titanic.csv。</p>
</div>
<p><code>df</code> 现在保存了 titanic 数据集的 DataFrame，接下来可以使用 <code>DataFrame.head()</code> 函数对数据集的首五行进行输出：</p>
<figure class="highlight python"><table><tr><td class="code"><pre><span class="line"><span class="built_in">print</span>(df.head())</span><br></pre></td></tr></table></figure>
<h1 id="将数据进行概述"><a href="#将数据进行概述" class="headerlink" title="将数据进行概述"></a>将数据进行概述</h1><p>将所有数据进行输出显然不科学，数据量太大，人脑也无法对其进行分析。我们可以通过 <code>DataFrame.describe()</code> 函数对数据进行概述，他将会返回一个统计表供我们了解整个数据中的各项数值</p>
<figure class="highlight python"><table><tr><td class="code"><pre><span class="line"><span class="built_in">print</span>(df.describe())</span><br></pre></td></tr></table></figure>
<p><code>describe</code> 函数只会对数值列表进行统计，非数值数据将会被掠过。他将会列出以下几个数据供分析：</p>
<figure class="highlight plaintext"><table><tr><td class="code"><pre><span class="line">         Survived      Pclass         Age  Siblings/Spouses  Parents/Children       Fare  </span><br><span class="line">count  887.000000  887.000000  887.000000        887.000000        887.000000  887.00000  </span><br><span class="line">mean     0.385569    2.305524   29.471443          0.525366          0.383315   32.30542  </span><br><span class="line">std      0.487004    0.836662   14.121908          1.104669          0.807466   49.78204  </span><br><span class="line">min      0.000000    1.000000    0.420000          0.000000          0.000000    0.00000  </span><br><span class="line">25%      0.000000    2.000000   20.250000          0.000000          0.000000    7.92500  </span><br><span class="line">50%      0.000000    3.000000   28.000000          0.000000          0.000000   14.45420  </span><br><span class="line">75%      1.000000    3.000000   38.000000          1.000000          0.000000   31.13750  </span><br><span class="line">max      1.000000    3.000000   80.000000          8.000000          6.000000  512.32920  </span><br></pre></td></tr></table></figure>
<ul>
<li>Count - 数据总数量</li>
<li>Mean - 平均值</li>
<li>Std - 标准平方差（Standard Deviation），用于衡量数据的离散状态）</li>
<li>Min - 最小值</li>
<li>25% - 25 分位数</li>
<li>50% - 50 分位数 / 中位数</li>
<li>75% - 75 分位数</li>
<li>Max - 最大值</li>
</ul>
<div class="note primary flat"><p>We use the Pandas describe method to start building some intuition about our data.</p>
<p>我们使用Pandas的describe方法来开始建立对我们的数据的一些直观认识。</p>
</div>
<h1 id="选择一个字段"><a href="#选择一个字段" class="headerlink" title="选择一个字段"></a>选择一个字段</h1><p>我们需要对数据集中的一些列进行操作，如果需要选择其中一个列，我们使用方括号和列名 <code>[&#39;列名&#39;]</code>，下边的例子就是选择乘客票价的列表</p>
<figure class="highlight python"><table><tr><td class="code"><pre><span class="line">col = df[<span class="string">&#x27;Fare&#x27;</span>]</span><br><span class="line"><span class="built_in">print</span>(col)</span><br></pre></td></tr></table></figure>
<p>我们将 DataFrame 中的某一列称之为 <code>Series</code>，Series 也是 DataFrame 的一种，但是只有一列数据。</p>
<figure class="highlight plaintext"><table><tr><td class="code"><pre><span class="line">0       7.2500</span><br><span class="line">1      71.2833</span><br><span class="line">2       7.9250</span><br><span class="line">3      53.1000</span><br><span class="line">4       8.0500</span><br><span class="line">        ...   </span><br><span class="line">882    13.0000</span><br><span class="line">883    30.0000</span><br><span class="line">884    23.4500</span><br><span class="line">885    30.0000</span><br><span class="line">886     7.7500</span><br><span class="line">Name: Fare, Length: 887, dtype: float64</span><br></pre></td></tr></table></figure>
<h1 id="选择多个字段"><a href="#选择多个字段" class="headerlink" title="选择多个字段"></a>选择多个字段</h1><p>我们当然可以选择数据集中的多个列来进行操作，创建一个小的 DataFrame。比如说我们选择 <code>Age, Sex, Survived</code> 列作为我们的小 DataFrame。要选取多个列，我们需要使用两个方括号 <code>[[&#39;列名1&#39;, &#39;列名2&#39;, ...]]</code> 来完成，如下所示：</p>
<figure class="highlight python"><table><tr><td class="code"><pre><span class="line">small_df = df[[<span class="string">&#x27;Age&#x27;</span>, <span class="string">&#x27;Sex&#x27;</span>, <span class="string">&#x27;Survived&#x27;</span>]]</span><br><span class="line"><span class="built_in">print</span>(small_df.head())</span><br></pre></td></tr></table></figure>
<div class="note info flat"><p>当选择一列时，我们使用一个方括号，如果选择多列时，我们需要使用两个方括号。</p>
</div>
<figure class="highlight plaintext"><table><tr><td class="code"><pre><span class="line">    Age     Sex  Survived</span><br><span class="line">0  22.0    male         0</span><br><span class="line">1  38.0  female         1</span><br><span class="line">2  26.0  female         1</span><br><span class="line">3  35.0  female         1</span><br><span class="line">4  35.0    male         0</span><br></pre></td></tr></table></figure>
<h1 id="创建一个字段"><a href="#创建一个字段" class="headerlink" title="创建一个字段"></a>创建一个字段</h1><p>我们经常希望我们的数据以比其原始格式稍微不同的方式呈现。例如，我们的数据中的乘客性别是一个字符串（”male” 或 “female”）。这对于人类来说很容易阅读，但当我们稍后对数据进行计算时，我们希望将其表示为布尔值（True 和 False）。</p>
<p>上面提到，我们可以通过选取一列获得一个 pandas Series 类型数据。在这基础上，我们可以直接对整个 Series 进行一些逻辑操作，如：</p>
<figure class="highlight python"><table><tr><td class="code"><pre><span class="line"><span class="built_in">print</span>(df[<span class="string">&#x27;Sex&#x27;</span>] == <span class="string">&#x27;male&#x27;</span>)</span><br></pre></td></tr></table></figure>
<p>执行这段操作之后，相当于 Series 会将整个数据集中的每一行取进行对比，如果是 <code>male</code> 则为 <code>True</code> 反之为 <code>False</code>，最后得到一个新的 Series</p>
<figure class="highlight plaintext"><table><tr><td class="code"><pre><span class="line">0       True</span><br><span class="line">1      False</span><br><span class="line">2      False</span><br><span class="line">3      False</span><br><span class="line">4       True</span><br><span class="line">       ...  </span><br><span class="line">882     True</span><br><span class="line">883    False</span><br><span class="line">884    False</span><br><span class="line">885     True</span><br><span class="line">886     True</span><br><span class="line">Name: Sex, Length: 887, dtype: bool</span><br></pre></td></tr></table></figure>
<p>那现在就需要创建一个列来保存这些数据，我们可以直接通过和选取列相同的代码来执行这个操作</p>
<figure class="highlight python"><table><tr><td class="code"><pre><span class="line">df[<span class="string">&#x27;Male&#x27;</span>] = df[<span class="string">&#x27;Sex&#x27;</span>] == <span class="string">&#x27;male&#x27;</span></span><br></pre></td></tr></table></figure>
<p>以上这行代码将会在 df 数据集中创建一个新的数据列 <code>Male</code>。然后保存 <code>df[&#39;Sex&#39;] == &#39;male&#39;</code> 的结果。</p>
<p>同理，我们可以创建一个用来判断用户是否是头等舱的数据集</p>
<figure class="highlight python"><table><tr><td class="code"><pre><span class="line">df[<span class="string">&#x27;First Class&#x27;</span>] = df[<span class="string">&#x27;Pclass&#x27;</span>] == <span class="number">1</span></span><br></pre></td></tr></table></figure></article><div class="post-copyright"><div class="post-copyright__author"><span class="post-copyright-meta"><i class="fas fa-circle-user fa-fw"></i>文章作者: </span><span class="post-copyright-info"><a href="https://kingsmai.github.io">小麦 Andrew Xiaomai</a></span></div><div class="post-copyright__type"><span class="post-copyright-meta"><i class="fas fa-square-arrow-up-right fa-fw"></i>文章链接: </span><span class="post-copyright-info"><a href="https://kingsmai.github.io/2023/12/18/ML-3-Pandas-%E6%95%B0%E6%8D%AE%E8%AF%BB%E5%8F%96%E4%B8%8E%E5%A4%84%E7%90%86/">https://kingsmai.github.io/2023/12/18/ML-3-Pandas-数据读取与处理/</a></span></div><div class="post-copyright__notice"><span class="post-copyright-meta"><i class="fas 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class="toc-content"><ol class="toc"><li class="toc-item toc-level-1"><a class="toc-link" href="#%E4%BB%80%E4%B9%88%E6%98%AF-Pandas"><span class="toc-number">1.</span> <span class="toc-text">什么是 Pandas</span></a></li><li class="toc-item toc-level-1"><a class="toc-link" href="#%E8%AF%BB%E5%8F%96%E6%95%B0%E6%8D%AE"><span class="toc-number">2.</span> <span class="toc-text">读取数据</span></a></li><li class="toc-item toc-level-1"><a class="toc-link" href="#%E5%B0%86%E6%95%B0%E6%8D%AE%E8%BF%9B%E8%A1%8C%E6%A6%82%E8%BF%B0"><span class="toc-number">3.</span> <span class="toc-text">将数据进行概述</span></a></li><li class="toc-item toc-level-1"><a class="toc-link" href="#%E9%80%89%E6%8B%A9%E4%B8%80%E4%B8%AA%E5%AD%97%E6%AE%B5"><span class="toc-number">4.</span> <span class="toc-text">选择一个字段</span></a></li><li class="toc-item toc-level-1"><a class="toc-link" href="#%E9%80%89%E6%8B%A9%E5%A4%9A%E4%B8%AA%E5%AD%97%E6%AE%B5"><span class="toc-number">5.</span> <span class="toc-text">选择多个字段</span></a></li><li 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